Problem
Research questions and friction points this paper is trying to address.
jerk
chunked vision language action policies
operational-space
GROOVE: Geometry-Guided Reduction of Operational-Space Jerk in VLA Execution
🤖 AI Summary
为减少VLA执行中的抖动，GROOVE通过在线调节三维末端执行器路径，并使用二次规划选择最优方向，从而降低抖动并保持任务成功率。
📝 Abstract
Chunked vision language action (VLA) policies execute several commands per query, but jerk within chunks and across replanning boundaries can induce oscillatory motion and sharp actuator transients. We present GROOVE, an online regulator that searches directional correction regions around the raw three dimensional end effector (EEF) path, without retraining or additional VLA inference. It optimizes the new chunk using delivered commands as boundary conditions, reducing boundary and within chunk jerk while bounding cumulative translation and local axis angle deviation from the raw plan after every command. Using quadratic programs (QPs), GROOVE generates a cube reference and thirteen directional candidates, then selects the one with the lowest command space jerk under a reference relative deviation cap. On a held out LIBERO benchmark, GROOVE achieves the largest reductions among the evaluated methods, reducing translational and rotational EEF jerk by 33.02% and 43.42%, respectively, with task success of 95.75% versus 93.75% for raw execution. Across 50 matched UR5e pairs with measured execution timing, it reduces translational and rotational tool center point (TCP) jerk by 16.39% and 19.49% and joint current slew by 29.09%.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Geometry-Guided
Operational-Space Jerk
VLA Execution
Quadratic Programs
Directional Correction
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